LIDARs are widely applied to systems such as an autopilot control system. The LIDAR can quickly establish a three-dimensional model of a vehicle surrounding environment by laser scanning, to provide basic data for high-precision cartography, obstacle recognition, and precise vehicle positioning, thus perceiving a vehicle traveling environment. When an object such as a self-driving car on which the LIDAR is configured is in a motion state, a three-dimensional model of a vehicle traveling environment established by directly using laser point cloud data of an acquired laser point cloud is distorted, and cannot authentically reflect a vehicle traveling environment of the self-driving car at a particular target time. Therefore, the laser point cloud acquired by the LIDAR cannot be directly used; instead, coordinates of a laser point need to be transformed into coordinates at a target time via motion compensation. A presently common motion compensation method is: pre-establishing a transformation relation tree for describing coordinate transformation relations between different acquisition time points, and selecting an acquisition time point of the first laser point as a target time. During the motion compensation on the laser point cloud data, each laser point in the laser point cloud is traversed, and a coordinate transformation relation corresponding to each laser point to the target time is separately queried from the transformation relation tree according to the acquisition time point of each laser point, to respectively transform coordinates of each laser point to the target time.
However, there are massive laser points, and each laser point in the laser point cloud requires a query process during the motion compensation. As a result, the overheads sharply increase. It is difficult to meet an extremely high requirement on real-time performance of operations in a system such as the autopilot control system, thus affecting the stability and safety of the system.